That we are awash in information is an often used, though certainly correct axiom of the early 21st century. The ubiquity of information access portals in everyday life provides the potential for connecting meaning to any experience or data set. Potential meaning is the key tenant here. How information sets are evaluated sorted and searched, remains the ultimate determinant of their actual value to the user.
Information in this modern sense may be considered as a two component entity; the first being the actual data it embodies, the second being its accessibility, based on identifying tags, anchors, or fields.
The exponential increases in both data production and storage capabilities have matched, not surprisingly, very well since “Moore's Law” tales have circulated through computational communities. Data search technologies, how information is sorted and accessed, however, have experienced a much more varied history. Though the success of market leaders' search algorithms, such as Google's “page-rank,” belie their effectiveness, the increasing volume and complexity of modern information structure has led to increased user dissatisfaction and frustration. Three significant points of current search algorithm dissatisfaction are search algorithm dissatisfaction are search output discrepancies, search output bias, and an incongruous match of search interface with brain heuristic functioning.
Search output discrepancies may result from a circumstance referred to as the “local search problem.” This problem arises when global data sets containing extrinsic information are not consistently cross checked or “curated” with local data sets containing intrinsic information. For example, a search for vacation destinations may yield inconsistent output if not updated with local information such as prices, business hours and patron ratings.
Search output bias may result primarily from either (1) discontinuities in search parameters weightings between the user and the search algorithm, or (2) misguiding parameter weightings through “search engine optimization” practices. In either case, miscommunication or lack of clear communication between user input and search algorithm programming may skew output away from the user's intentions.
As search engine and social networking leaders battle the concept of truth and validity on the Internet, a potential problem looms for society as a whole. The popularity of social networking sites has made searching within peer preference databases very effective and appealing. A search conducted within a social network database consisting of peers with similar preferences (intrinsic data) is highly likely to produce user preferable results. Such a behavior, however, limits variation by culling preference outliers. As in biology, any system lacking diversity, while successful within its native context, is resistant to change, slow to adapt, and quickly expends its resources.
Finally, search is a process to learn. Psychological studies of our mind's processing methods maintain that we interpret and organize stimuli based on heuristic schemas. These heuristic schemas contain impressions and rules of thumb that are based on our collective experiences. Commonly statistical methodologies are utilized to explain the occurrence of an activity(s), a decision(s), or a behavior(s). A common form of explanation through statistical measurement is through the use of multivariate mathematical modeling. Within the mathematical models it is very common to have 2-4 variables that explain the vast majority of the phenomenon under investigation. When we form opinions or judgments, we commonly utilize a couple of variables that form the heuristic schema that guide our decision making for a given decision topic.
To better match the processing methodology of our brain, an enhanced method and interface of search will enable a quicker, more sensitive, and more exhaustive search process. Currently, search is guided by a serial (“step-by-step”) process. Each step produces a list of results based on one dimension or variable guiding the search command interface. In order to match our mind's organized discovery process and experiential store of knowledge, the search interface could allow the simultaneous expression of multi-dimensional discovery or reasoning. The search process could be expressed as a coordinate within an area that is bound by the multi-dimensional vectors that represent the most important characteristics that involve the topic under investigation. Each vector is a mathematical expression denoting a combination of magnitude and direction. Based on the number of variables that are utilized to characterize the topic under investigation, the area of intersection between the vectors can range from uni-dimensional vector reflection to a multi-dimensional area of expression. We can use a coordinate within the range or area of expression to strengthen or reduce the importance of a variable under investigation. This toggling of coordinate placement allows the search process to maintain a view that simultaneously engages the critical characteristics that govern an intended inquiry.
Through this process the view of search will more closely match the heuristic management of new stimuli. It allows a more graphical representation of the multi-dimensional decision making process. It should speed the search process by maintaining a multi-dimensional view of the critical characteristics that are intended to guide the search. It may also enable a more exhaustive search due to the graphical sensitivity.
There exists an apparent need for an interface between user and search algorithms which would allow the joining of discontinuous data sets, an intuitive means of user awareness and manipulation of search parameter weightings, as well as an effective means of searching across intrinsic and extrinsic data sets.
Finally, the search interface described herein provides an intuitive and fundamentally more accurate means for image search. As today's information schema become increasingly graphically structured, a need arises to be able to search within this graphical landscape. Currently, image search requires a series of translations, such as from user cognition to language representation, from language representation to image meta-tag searching, and from image meta-tag searching to search result structuring. Each one of these translational steps, introduce a degree of error, distancing original intent from search output. From an operational perspective, this disconnect between intent and output introduce an unaddressed need at two levels. One, the user either is not connected with the most accurate solution to their search, or must take additional time to search. Two, information providers, observing user search behavior, will misinterpret the user's behavior intent relationship, and thus, inaccurately serving their client's needs.